Fourier analysis of stationary time series in function space

TL;DR

This paper develops a frequency domain framework for analyzing the second-order structure of stationary functional time series using spectral density operators and the functional DFT, enabling statistical inference without structural assumptions.

Contribution

It introduces a spectral density operator for functional data, derives an asymptotic Gaussian representation of the functional DFT, and constructs consistent estimators for spectral analysis.

Findings

Asymptotic Gaussian representation of the functional DFT.

Consistent estimators for the spectral density operator.

Central limit theorems for mean and long-run covariance.

Abstract

We develop the basic building blocks of a frequency domain framework for drawing statistical inferences on the second-order structure of a stationary sequence of functional data. The key element in such a context is the spectral density operator, which generalises the notion of a spectral density matrix to the functional setting, and characterises the second-order dynamics of the process. Our main tool is the functional Discrete Fourier Transform (fDFT). We derive an asymptotic Gaussian representation of the fDFT, thus allowing the transformation of the original collection of dependent random functions into a collection of approximately independent complex-valued Gaussian random functions. Our results are then employed in order to construct estimators of the spectral density operator based on smoothed versions of the periodogram kernel, the functional generalisation of the periodogram matrix. The consistency and asymptotic law of these estimators are studied in detail. As immediate consequences, we obtain central limit theorems for the mean and the long-run covariance operator of a stationary functional time series. Our results do not depend on structural modelling assumptions, but only functional versions of classical cumulant mixing conditions, and are shown to be stable under discrete observation of the individual curves.